BMIQ_all: TCGA methylation data with IDHm, IDHWT and CD34+
BMIQ_CD34: Methylome from GSE103008, CD34+ Methylation
BMIQ_Wang_Feng: Methylome from Koichi’s study
BMIQ_Whiele: Methylome from Whiele’s study
Wang_Feng_RNAseq: Gene expression from Koichi’s study
Loading of gene promoter annotation from Blueprint project
Loading CpGs annotation from Illumina manifest
Loading chromatin conformation annotation
Creation of Granges object with coordinates
Overlapping of the different sources of annotations
gene_list <- c("CEBPA", "CPT1A", "CPT2", "SLC25A20", "AKT2", "PPARGC1A", "PTEN")
Blueprint <- read.csv("../BLUEPRINT_fragments_good.tsv", sep = "\t") %>%
dplyr::select(., "gene_names":"type", "gene_type") %>%
separate_rows(., gene_names, sep = " ") %>%
separate_rows(., gene_type, sep = " ") %>%
unique(.)
pchic <- prepare_pchic()
PCHiC_bed <- unique(rbind(pchic[, c(1:3, 5)], pchic[, c(6:8, 10)]))
PCHiC_GRange <- GRanges(
seqnames = PCHiC_bed$chr,
IRanges(start = PCHiC_bed$start, end = PCHiC_bed$end),
Gene_Pchic = PCHiC_bed$Name,
start_fragment = PCHiC_bed$start,
end_fragment = PCHiC_bed$end
)
PCHiC_GRange$ID <- paste(PCHiC_bed$chr, PCHiC_bed$start, sep = "_")
colnames(pchic) <- c("chr_bait", "start_bait", "end_bait", "ID_bait", "Name_bait", "chr_oe", "start_oe", "end_oe", "ID_oe", "Name_oe")
pchic$IDbait <- paste(pchic$chr_bait, pchic$start_bait, sep = "_")
pchic$IDoe <- paste(pchic$chr_oe, pchic$start_oe, sep = "_")
Blueprint_Granges <- GRanges(
seqnames = Blueprint$chr,
ranges = IRanges(start = Blueprint$start, end = Blueprint$end),
Blueprint_gene_names = Blueprint$gene_names,
type = Blueprint$type,
gene_type = Blueprint$gene_type
)
## ILLUMINA 450K
anno_450 <- read.csv("~/Illumina_Manifest/HumanMethylation450_15017482_v1-2.csv", skip = 7) %>%
dplyr::select(., "Name", "CHR", "MAPINFO", "UCSC_RefGene_Name", "UCSC_RefGene_Group", "Relation_to_UCSC_CpG_Island") %>%
dplyr::filter(., CHR != "") %>%
separate_rows(., UCSC_RefGene_Name, UCSC_RefGene_Group, sep = ";")
CpGs_Granges_450 <- GRanges(
seqnames = anno_450$CHR,
ranges = IRanges(anno_450$MAPINFO, anno_450$MAPINFO +1),
CpG = anno_450$Name,
Illumina_Gene_name = anno_450$UCSC_RefGene_Name,
position = anno_450$UCSC_RefGene_Group,
Island = anno_450$Relation_to_UCSC_CpG_Island
)
overlaps_CpGs_Blueprint_450 <- findOverlaps(Blueprint_Granges, CpGs_Granges_450)
match_hit_CpGs_Blueprint_450 <- data.frame(mcols(Blueprint_Granges[queryHits(overlaps_CpGs_Blueprint_450),]),
data.frame(mcols(CpGs_Granges_450[subjectHits(overlaps_CpGs_Blueprint_450),])))
match_hit_CpGs_Blueprint_promoter_450 <- dplyr::filter(match_hit_CpGs_Blueprint_450, type == "P")
anno_promoter_450 <- dplyr::filter(anno_450, UCSC_RefGene_Group == "TSS1500" | UCSC_RefGene_Group == "TSS200")
overlaps_CpGs_Pchic_450 <- findOverlaps(CpGs_Granges_450, PCHiC_GRange)
matchit_CpGs_Pchic_450 <- data.frame(mcols(CpGs_Granges_450[queryHits(overlaps_CpGs_Pchic_450),]),
data.frame(mcols(PCHiC_GRange[subjectHits(overlaps_CpGs_Pchic_450),])))
gene_universe_450K <- unique(match_hit_CpGs_Blueprint_promoter_450$Blueprint_gene_names)
#########################
## ILLUMINA EPIC
anno_EPIC <- read.csv("~/Illumina_Manifest/infinium-methylationepic-v-1-0-b5-manifest-file.csv", skip = 7) %>%
dplyr::select(., "Name", "CHR", "MAPINFO", "UCSC_RefGene_Name", "UCSC_RefGene_Group", "Relation_to_UCSC_CpG_Island") %>%
dplyr::filter(., CHR != "") %>%
separate_rows(., UCSC_RefGene_Name, UCSC_RefGene_Group, sep = ";")
CpGs_Granges_EPIC <- GRanges(
seqnames = anno_EPIC$CHR,
ranges = IRanges(anno_EPIC$MAPINFO, anno_EPIC$MAPINFO +1),
CpG = anno_EPIC$Name,
Illumina_Gene_name = anno_EPIC$UCSC_RefGene_Name,
position = anno_EPIC$UCSC_RefGene_Group,
Island = anno_EPIC$Relation_to_UCSC_CpG_Island
)
overlaps_CpGs_Blueprint_EPIC <- findOverlaps(Blueprint_Granges, CpGs_Granges_EPIC)
match_hit_CpGs_Blueprint_EPIC <- data.frame(mcols(Blueprint_Granges[queryHits(overlaps_CpGs_Blueprint_EPIC),]),
data.frame(mcols(CpGs_Granges_EPIC[subjectHits(overlaps_CpGs_Blueprint_EPIC),])))
match_hit_CpGs_Blueprint_promoter_EPIC <- dplyr::filter(match_hit_CpGs_Blueprint_EPIC, type == "P")
anno_promoter_EPIC <- dplyr::filter(anno_EPIC, UCSC_RefGene_Group == "TSS1500" | UCSC_RefGene_Group == "TSS200")
overlaps_CpGs_Pchic_EPIC <- findOverlaps(CpGs_Granges_EPIC, PCHiC_GRange)
matchit_CpGs_Pchic_EPIC <- data.frame(mcols(CpGs_Granges_EPIC[queryHits(overlaps_CpGs_Pchic_EPIC),]),
data.frame(mcols(PCHiC_GRange[subjectHits(overlaps_CpGs_Pchic_EPIC),])))
gene_universe_EPIC <- unique(match_hit_CpGs_Blueprint_promoter_EPIC$Blueprint_gene_names)
##########################
overlaps_Blueprint_Pchic <- findOverlaps(Blueprint_Granges, PCHiC_GRange)
matchit_Blueprint_Pchic <- data.frame(mcols(Blueprint_Granges[queryHits(overlaps_Blueprint_Pchic),]),
data.frame(mcols(PCHiC_GRange[subjectHits(overlaps_Blueprint_Pchic),]))) %>%
dplyr::filter(., type == "P")
signature <- read.csv("../DATA/BPmetCan.txt", sep = "\t")
rownames(signature) <- signature$CpGs
signature <- signature[,-1]
Preparation of the data
Phenotype_BMIQ_CD34_IDHm_WT <- read.csv("../../TCGA_Connections/Phenotype_met.csv") %>%
dplyr::filter(., WT1 == "WT1WT", DNMT3A == "DNMT3AWT", TET2 == "TET2WT", FLT3 == "FLT3AWT")
Phenotype_BMIQ_CD34_IDHm_WT$Phenotype <- ifelse(Phenotype_BMIQ_CD34_IDHm_WT$IDH == "IDHm", "IDHm", "WT")
Phenotype_BMIQ_CD34_IDHm_WT <- dplyr::select(Phenotype_BMIQ_CD34_IDHm_WT, c(X, Phenotype)) %>%
rbind(data.frame(X = colnames(BMIQ_CD34), Phenotype = rep("CD34", 6)))
Phenotype_BMIQ_CD34_IDHm_WT$X <- str_replace(Phenotype_BMIQ_CD34_IDHm_WT$X, "-", ".")
Phenotype_BMIQ_CD34_IDHm_WT$X <- str_replace(Phenotype_BMIQ_CD34_IDHm_WT$X, "-", ".")
BMIQ_CD34_IDHm_WT <- BMIQ_all %>%
dplyr::select(., Phenotype_BMIQ_CD34_IDHm_WT$X)
res <- epidish(as.matrix(BMIQ_CD34_IDHm_WT), as.matrix(signature), method = "RPC")
Fres <- as.data.frame(res$estF)
Fres <- cbind(Sample = rownames(Fres), Fres)
Fres[, -1] <- round(Fres[, -1], 3)
deconvolution_violin_TCGA_DATA <- data.frame(Cell_Type = rep(colnames(Fres[,c(2:12)]), each = length(rownames(Fres))),
Values = Fres[,c(2:12)] %>% c(.) %>% unlist(.) %>% as.vector(.))
deconvolution_violin_TCGA_DATA$Values <- round(deconvolution_violin_TCGA_DATA$Values, 1)
ggplot(deconvolution_violin_TCGA_DATA, aes(x=Cell_Type, y=Values, fill=Cell_Type), las = 2) +
geom_violin() +
ggtitle("TCGA all sample")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
BMIQ_all_high_variance <- Focus_high_variance_CpGs(BMIQ_CD34_IDHm_WT, percentage = 1)
Focus_global_methylation(match_hit_CpGs_Blueprint_450, anno_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_all_high_variance, c("IDHm", "WT"), fill_colour = c("#00FF00", "#FF0000", "#0000FF", "#00FF00"))
Focus_Gene_global_methylation("CEBPA", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"))
Focus_Gene_global_methylation("CPT1A", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"))
Focus_Gene_global_methylation("CPT2", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"))
Focus_Gene_global_methylation("SLC25A20", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"))
Focus_Gene_global_methylation("PPARGC1A", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"))
Focus_Gene_global_methylation("AKT2", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"))
Focus_Gene_global_methylation("PTEN", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"))
Focus_gene_promoter_neighborhood_methylation("CEBPA", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"), matchit_CpGs_Pchic_450, pchic, match_hit_CpGs_Blueprint_450)
Focus_gene_promoter_neighborhood_methylation("CPT1A", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"), matchit_CpGs_Pchic_450, pchic, match_hit_CpGs_Blueprint_450)
Focus_gene_promoter_neighborhood_methylation("CPT2", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"), matchit_CpGs_Pchic_450, pchic, match_hit_CpGs_Blueprint_450)
Focus_gene_promoter_neighborhood_methylation("SLC25A20", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"), matchit_CpGs_Pchic_450, pchic, match_hit_CpGs_Blueprint_450)
## [1] "No Cpgs found in neighbor of promoter of SLC25A20"
## [1] FALSE
Focus_gene_promoter_neighborhood_methylation("PPARGC1A", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"), matchit_CpGs_Pchic_450, pchic, match_hit_CpGs_Blueprint_450)
## [1] "No Cpgs found in neighbor of promoter of PPARGC1A"
## [1] FALSE
Focus_gene_promoter_neighborhood_methylation("AKT2", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"), matchit_CpGs_Pchic_450, pchic, match_hit_CpGs_Blueprint_450)
Focus_gene_promoter_neighborhood_methylation("PTEN", match_hit_CpGs_Blueprint_promoter_450, anno_promoter_450, Phenotype_BMIQ_CD34_IDHm_WT, BMIQ_CD34_IDHm_WT, c("IDHm", "WT"), c("#00FF00", "#FF0000", "#0000FF"), matchit_CpGs_Pchic_450, pchic, match_hit_CpGs_Blueprint_450)
BMIQ_TCGA_analysis <- Differential_analysis(Phenotype_BMIQ_CD34_IDHm_WT$Phenotype, BMIQ_CD34_IDHm_WT)
## 1 done
## 2 done
## 3 done
Response_diff_hypermet_in_IDHm <- BMIQ_TCGA_analysis[["IDHm-WT"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC > 0.3)
Response_diff_hypermet_in_WT <- BMIQ_TCGA_analysis[["IDHm-WT"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC < 0.3)
Genes_hypermetylated_in_IDHm <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_IDHm$ID, match_hit_CpGs_Blueprint_promoter_450)
Genes_hypermetylated_in_IDHm_enhancer <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_IDHm$ID,
matchit_CpGs_Pchic_450,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_450)
Genes_hypermetylated_in_WT <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_WT$ID, match_hit_CpGs_Blueprint_promoter_450)
Genes_hypermetylated_in_WT_enhancer <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_WT$ID,
matchit_CpGs_Pchic_450,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_450)
Genes_hypermetylated_in_IDHm_ego <- enrichGO(
gene = Genes_hypermetylated_in_IDHm,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_450K
)
Genes_hypermetylated_in_IDHm_enhancer_ego <- enrichGO(
gene = Genes_hypermetylated_in_IDHm_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_450K
)
Genes_hypermetylated_in_WT_ego <- enrichGO(
gene = Genes_hypermetylated_in_WT,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_450K
)
Genes_hypermetylated_in_WT_enhancer_ego <- enrichGO(
gene = Genes_hypermetylated_in_WT_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_450K
)
Genes_hypermetylated_in_IDHm_promoter_and_enhancer_ego <- enrichGO(
gene = unique(c(Genes_hypermetylated_in_IDHm, Genes_hypermetylated_in_IDHm_enhancer)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_450K
)
Genes_hypermetylated_in_WT_promoter_and_enhancer_ego <- enrichGO(
gene = unique(c(Genes_hypermetylated_in_WT, Genes_hypermetylated_in_WT_enhancer)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_450K
)
dotplot(Genes_hypermetylated_in_IDHm_ego, showCategory = 30, title = "Hypermet IDHm", font.size = 6)
dotplot(Genes_hypermetylated_in_IDHm_enhancer_ego, showCategory = 30, title = "Hypermet Neighborhood IDHm")
dotplot(Genes_hypermetylated_in_IDHm_promoter_and_enhancer_ego, showCategory = 30, title = "Prom & enhancer IDHm")
dotplot(Genes_hypermetylated_in_WT_ego, showCategory = 30, title = "Hypermet WT")
dotplot(Genes_hypermetylated_in_WT_enhancer_ego, showCategory = 30, title = "Hypermet Neighborhood WT")
dotplot(Genes_hypermetylated_in_WT_promoter_and_enhancer_ego, showCategory = 30, title = "Prom & enhancer WT")
volcanoplot_methylation(BMIQ_TCGA_analysis[["IDHm-WT"]], match_hit_CpGs_Blueprint_450, "IDHm vs WT")
DATA_for_PCA <- t(as.data.frame(BMIQ_Whiele))
res.pca <- PCA(DATA_for_PCA, ncp = 3, graph = TRUE)
fviz_eig(res.pca, addlabels = TRUE)
BMIQ_Whiele_high_variance <- Focus_high_variance_CpGs(BMIQ_Whiele, percentage = 1)
Phenotype_Whiele$Phenotype <- ifelse(Phenotype_Whiele$Phenotype == "WT+2HG", "WTHG", Phenotype_Whiele$Phenotype)
ann_color <- list(
Phenotype = c(IDH2m = "red", WT = "blue", Control = "grey", WTHG = "green"))
Make_heatmap(BMIQ_Whiele, Phenotype_Whiele, "pearson", "All values", ann_color)
Make_heatmap(BMIQ_Whiele_high_variance, Phenotype_Whiele, "pearson", "High variance", ann_color)
res <- epidish(as.matrix(BMIQ_Whiele), as.matrix(signature), method = "RPC")
Fres <- as.data.frame(res$estF)
Fres <- cbind(Sample = rownames(Fres), Fres)
Fres[, -1] <- round(Fres[, -1], 3)
deconvolution_violin_Whiele_DATA <- data.frame(Cell_Type = rep(colnames(Fres[,c(2:12)]), each = length(rownames(Fres))),
Values = Fres[,c(2:12)] %>% c(.) %>% unlist(.) %>% as.vector(.))
deconvolution_violin_Whiele_DATA$Values <- round(deconvolution_violin_Whiele_DATA$Values, 1)
ggplot(deconvolution_violin_Whiele_DATA, aes(x=Cell_Type, y=Values, fill=Cell_Type), las = 2) +
geom_violin() +
ggtitle("All the sample")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Focus_global_methylation(match_hit_CpGs_Blueprint_EPIC, anno_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"))
Focus_Gene_global_methylation("CEBPA", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"))
Focus_Gene_global_methylation("CPT1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"))
Focus_Gene_global_methylation("CPT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"))
Focus_Gene_global_methylation("SLC25A20", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"))
Focus_Gene_global_methylation("PPARGC1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"))
Focus_Gene_global_methylation("AKT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"))
Focus_Gene_global_methylation("PTEN", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"))
Focus_gene_promoter_neighborhood_methylation("CEBPA", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
Focus_gene_promoter_neighborhood_methylation("CPT1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
Focus_gene_promoter_neighborhood_methylation("CPT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
Focus_gene_promoter_neighborhood_methylation("SLC25A20", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
## [1] "No Cpgs found in neighbor of promoter of SLC25A20"
## [1] FALSE
Focus_gene_promoter_neighborhood_methylation("PPARGC1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
## [1] "No Cpgs found in neighbor of promoter of PPARGC1A"
## [1] FALSE
Focus_gene_promoter_neighborhood_methylation("AKT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
Focus_gene_promoter_neighborhood_methylation("PTEN", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Whiele, BMIQ_Whiele, c("IDH2m", "WT"), c("#555555", "#FF0000", "#0000FF", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
BMIQ_Whiele_analysis <- Differential_analysis(Phenotype_Whiele$Phenotype, BMIQ_Whiele)
## 1 done
## 2 done
## 3 done
## 4 done
## 5 done
## 6 done
Response_diff_hypermet_in_IDH2m <- BMIQ_Whiele_analysis[["IDH2m-WT"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC > 0.3)
Response_diff_hypermet_in_WT_whiele <- BMIQ_Whiele_analysis[["IDH2m-WT"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC < 0.3)
Genes_hypermetylated_in_IDH2m <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_IDH2m$ID, match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_IDH2m_enhancer <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_IDH2m$ID,
matchit_CpGs_Pchic_EPIC,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_WT_whiele <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_WT_whiele$ID, match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_WT_enhancer_whiele <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_WT_whiele$ID,
matchit_CpGs_Pchic_EPIC,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_IDH2m_ego <- enrichGO(
gene = Genes_hypermetylated_in_IDH2m,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_IDH2m_enhancer_ego <- enrichGO(
gene = Genes_hypermetylated_in_IDH2m_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_WT_whiele_ego <- enrichGO(
gene = Genes_hypermetylated_in_WT_whiele,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_WT_enhancer_ego_whiele <- enrichGO(
gene = Genes_hypermetylated_in_WT_enhancer_whiele,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_IDH2m_promoter_and_enhancer_ego <- enrichGO(
gene = unique(c(Genes_hypermetylated_in_IDH2m, Genes_hypermetylated_in_IDH2m_enhancer)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_WT_promoter_and_enhancer_ego_whiele <- enrichGO(
gene = unique(c(Genes_hypermetylated_in_WT_whiele, Genes_hypermetylated_in_WT_enhancer_whiele)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
dotplot(Genes_hypermetylated_in_IDH2m_ego, showCategory = 30, title = "Hypermet IDHm")
dotplot(Genes_hypermetylated_in_IDH2m_enhancer_ego, showCategory = 30, title = "Hypermet Neighborhood IDHm", font.size = 9)
dotplot(Genes_hypermetylated_in_IDH2m_promoter_and_enhancer_ego, showCategory = 30, title = "Prom & enhancer IDHm")
dotplot(Genes_hypermetylated_in_WT_whiele_ego, showCategory = 30, title = "Hypermet WT")
dotplot(Genes_hypermetylated_in_WT_enhancer_ego_whiele, showCategory = 30, title = "Hypermet Neighborhood WT", font.size = 9)
dotplot(Genes_hypermetylated_in_WT_promoter_and_enhancer_ego_whiele, showCategory = 30, title = "Prom & enhancer WT")
volcanoplot_methylation(BMIQ_Whiele_analysis[["IDH2m-WT"]], match_hit_CpGs_Blueprint_EPIC, "IDH2m vs WT")
DATA_for_PCA <- t(as.data.frame(BMIQ_Wang_Feng))
res.pca <- PCA(DATA_for_PCA, ncp = 3, graph = TRUE)
fviz_eig(res.pca, addlabels = TRUE)
BMIQ_Wang_Feng_high_variance <- Focus_high_variance_CpGs(BMIQ_Wang_Feng, percentage = 1)
ann_color <- list(
Phenotype = c(Baseline = "blue", Non_Responder = "red", Responder = "green", Control = "grey"))
Make_heatmap(BMIQ_Wang_Feng, Phenotype_Wang_Feng, "pearson", "All values", ann_color)
Make_heatmap(BMIQ_Wang_Feng_high_variance, Phenotype_Wang_Feng, "pearson", "High variance", ann_color)
Phenotype_Wang_Feng_Control_Baseline <- Phenotype_Wang_Feng %>%
dplyr::filter(., Phenotype == "Baseline" | Phenotype == "Control")
BMIQ_Control_Baseline <- dplyr::select(BMIQ_Wang_Feng, Phenotype_Wang_Feng_Control_Baseline$Sample_number)
BMIQ_Control_Baseline_high_variance <- Focus_high_variance_CpGs(BMIQ_Control_Baseline, 1)
Phenotype_Wang_Feng_Responder_Non_Responder <- Phenotype_Wang_Feng %>%
dplyr::filter(., Phenotype != "Baseline" & Phenotype != "Control")
BMIQ_Responder_Non_Responder <- dplyr::select(BMIQ_Wang_Feng, Phenotype_Wang_Feng_Responder_Non_Responder$Sample_number)
BMIQ_Responder_Non_Responder_high_variance <- Focus_high_variance_CpGs(BMIQ_Responder_Non_Responder, 1)
Make_heatmap(BMIQ_Control_Baseline, Phenotype_Wang_Feng_Control_Baseline, "pearson", "Control vs Baseline", ann_color)
Make_heatmap(BMIQ_Control_Baseline_high_variance, Phenotype_Wang_Feng_Control_Baseline, "pearson", "Control vs Baseline High variance", ann_color)
Make_heatmap(BMIQ_Responder_Non_Responder, Phenotype_Wang_Feng_Responder_Non_Responder, "pearson", "Responder vs Non-Responder", ann_color)
Make_heatmap(BMIQ_Responder_Non_Responder_high_variance, Phenotype_Wang_Feng_Responder_Non_Responder, "pearson", "Responder vs Non-Responder High variance", ann_color)
Phenotype_Wang_Feng_Baseline <- Phenotype_Wang_Feng %>%
dplyr::filter(., Phenotype == "Baseline")
Phenotype_cluster <- read.csv("../DATA/Wang_Feng_DATA/Clustering_Baseline_phenotype.csv")
Phenotype_Wang_Feng_Baseline_clustered <- merge(Phenotype_Wang_Feng_Baseline, Phenotype_cluster, by.x = "Sample", by.y = "UPN", all.x = TRUE)
Phenotype_Wang_Feng_Baseline_clustered$Phenotype <- Phenotype_Wang_Feng_Baseline_clustered$Cluster
BMIQ_Baseline <- dplyr::select(BMIQ_Wang_Feng, Phenotype_Wang_Feng_Baseline_clustered$Sample_number)
BMIQ_Baseline_high_variance <- Focus_high_variance_CpGs(BMIQ_Baseline, 1)
#BMIQ_Baseline_high_variance_promoter <- Focus_high_variance_CpGs(BMIQ_Baseline_promoter, 1)
ann_color <- list(
Phenotype = c(cluster1 = "blue", cluster2 = "red"))
Make_heatmap(BMIQ_Baseline, Phenotype_Wang_Feng_Baseline_clustered, "pearson", "Baseline", ann_color)
Make_heatmap(BMIQ_Baseline_high_variance, Phenotype_Wang_Feng_Baseline_clustered, "pearson", "Baseline High variance", ann_color)
#Make_heatmap(BMIQ_Baseline_promoter, Phenotype_Wang_Feng_Baseline_clustered, "pearson", "Baseline promoter", ann_color)
#Make_heatmap(BMIQ_Baseline_high_variance_promoter, Phenotype_Wang_Feng_Baseline_clustered, "pearson", "Baseline High variance promoter", ann_color)
signature <- read.csv("../DATA/BPmetCan.txt", sep = "\t")
rownames(signature) <- signature$CpGs
signature <- signature[,-1]
res <- epidish(as.matrix(BMIQ_Wang_Feng), as.matrix(signature), method = "RPC")
Fres <- as.data.frame(res$estF)
Fres <- cbind(Sample = rownames(Fres), Fres)
Fres[, -1] <- round(Fres[, -1], 3)
deconvolution_violin_Koichi_Data <- data.frame(Cell_Type = rep(colnames(Fres[,c(2:12)]), each = length(rownames(Fres))),
Values = Fres[,c(2:12)] %>% c(.) %>% unlist(.) %>% as.vector(.))
deconvolution_violin_Koichi_Data$Values <- round(deconvolution_violin_Koichi_Data$Values, 1)
Baseline_sample <- Phenotype_Wang_Feng %>%
.[.$Phenotype == "Baseline", "Sample_number"]
res_Baseline <- epidish(as.matrix(BMIQ_Wang_Feng %>%
dplyr::select(., Baseline_sample)), as.matrix(signature), method = "RPC")
## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(Baseline_sample)` instead of `Baseline_sample` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
Fres_Baseline <- as.data.frame(res_Baseline$estF)
Fres_Baseline <- cbind(Sample = rownames(Fres_Baseline), Fres_Baseline)
Fres_Baseline[, -1] <- round(Fres_Baseline[, -1], 3)
deconvolution_violin_Baseline <- data.frame(Cell_Type = rep(colnames(Fres_Baseline[,c(2:12)]), each = length(rownames(Fres_Baseline))),
Values = Fres_Baseline[,c(2:12)] %>% c(.) %>% unlist(.) %>% as.vector(.))
deconvolution_violin_Baseline$Values <- round(deconvolution_violin_Baseline$Values, 1)
Responder_sample <- Phenotype_Wang_Feng %>%
.[.$Phenotype == "Responder", "Sample_number"]
res_Responder <- epidish(as.matrix(BMIQ_Wang_Feng %>%
dplyr::select(., Responder_sample)), as.matrix(signature), method = "RPC")
## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(Responder_sample)` instead of `Responder_sample` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
Fres_Responder <- as.data.frame(res_Responder$estF)
Fres_Responder <- cbind(Sample = rownames(Fres_Responder), Fres_Responder)
Fres_Responder[, -1] <- round(Fres_Responder[, -1], 3)
deconvolution_violin_Responder <- data.frame(Cell_Type = rep(colnames(Fres_Responder[,c(2:12)]), each = length(rownames(Fres_Responder))),
Values = Fres_Responder[,c(2:12)] %>% c(.) %>% unlist(.) %>% as.vector(.))
deconvolution_violin_Responder$Values <- round(deconvolution_violin_Responder$Values, 1)
Non_Responder_sample <- Phenotype_Wang_Feng %>%
.[.$Phenotype == "Non_Responder", "Sample_number"]
res_Non_Responder <- epidish(as.matrix(BMIQ_Wang_Feng %>%
dplyr::select(., Non_Responder_sample)), as.matrix(signature), method = "RPC")
## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(Non_Responder_sample)` instead of `Non_Responder_sample` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
Fres_Non_Responder <- as.data.frame(res_Non_Responder$estF)
Fres_Non_Responder <- cbind(Sample = rownames(Fres_Non_Responder), Fres_Non_Responder)
Fres_Non_Responder[, -1] <- round(Fres_Non_Responder[, -1], 3)
deconvolution_violin_Non_Responder <- data.frame(Cell_Type = rep(colnames(Fres_Non_Responder[,c(2:12)]), each = length(rownames(Fres_Non_Responder))),
Values = Fres_Non_Responder[,c(2:12)] %>% c(.) %>% unlist(.) %>% as.vector(.))
deconvolution_violin_Non_Responder$Values <- round(deconvolution_violin_Non_Responder$Values, 1)
Control_sample <- Phenotype_Wang_Feng %>%
.[.$Phenotype == "Control", "Sample_number"]
res_Control <- epidish(as.matrix(BMIQ_Wang_Feng %>%
dplyr::select(., Control_sample)), as.matrix(signature), method = "RPC")
## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(Control_sample)` instead of `Control_sample` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
Fres_Control <- as.data.frame(res_Control$estF)
Fres_Control <- cbind(Sample = rownames(Fres_Control), Fres_Control)
Fres_Control[, -1] <- round(Fres_Control[, -1], 3)
deconvolution_violin_Control <- data.frame(Cell_Type = rep(colnames(Fres_Control[,c(2:12)]), each = length(rownames(Fres_Control))),
Values = Fres_Control[,c(2:12)] %>% c(.) %>% unlist(.) %>% as.vector(.))
deconvolution_violin_Control$Values <- round(deconvolution_violin_Control$Values, 1)
ggplot(deconvolution_violin_Koichi_Data, aes(x=Cell_Type, y=Values, fill=Cell_Type), las = 2) +
geom_violin() +
ggtitle("All the sample")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
ggplot(deconvolution_violin_Baseline, aes(x=Cell_Type, y=Values, fill=Cell_Type), las = 2) +
geom_violin() +
ggtitle("Baseline")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
ggplot(deconvolution_violin_Responder, aes(x=Cell_Type, y=Values, fill=Cell_Type), las = 2) +
geom_violin() +
ggtitle("Responder")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
ggplot(deconvolution_violin_Non_Responder, aes(x=Cell_Type, y=Values, fill=Cell_Type), las = 2) +
geom_violin() +
ggtitle("Non_Responder")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
ggplot(deconvolution_violin_Control, aes(x=Cell_Type, y=Values, fill=Cell_Type), las = 2) +
geom_violin() +
ggtitle("Control")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Focus_global_methylation(match_hit_CpGs_Blueprint_EPIC, anno_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng_high_variance, c("Baseline", "Control"), c("#0000FF", "#555555", "#FF0000", "#00FF00"))
Focus_global_methylation(match_hit_CpGs_Blueprint_EPIC, anno_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline_high_variance, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"))
cluster1_sample <- Phenotype_Wang_Feng_Baseline_clustered %>%
dplyr::filter(., Phenotype == "cluster1") %>%
.$Sample_number
BMIQ_cluster1 <- BMIQ_Baseline %>%
dplyr::select(., cluster1_sample)
## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(cluster1_sample)` instead of `cluster1_sample` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
Responder_sample <- Phenotype_Wang_Feng %>%
dplyr::filter(., Phenotype == "Responder") %>%
.$Sample_number
BMIQ_Responder <- BMIQ_Wang_Feng %>%
dplyr::select(., Responder_sample)
BMIQ_cluster1_Responder <- cbind(BMIQ_cluster1, BMIQ_Responder)
Phenotype_Responder_cluster1 <- data.frame("Sample" = c(cluster1_sample, Responder_sample), "Phenotype" = c(rep("cluster1", length(cluster1_sample)), rep("Responder", length(Responder_sample))))
BMIQ_cluster1_Responder_high_variance <- Focus_high_variance_CpGs(BMIQ_cluster1_Responder)
Focus_global_methylation(match_hit_CpGs_Blueprint_EPIC, anno_EPIC, Phenotype_Responder_cluster1, BMIQ_cluster1_Responder_high_variance, c("cluster1", "Responder"), c("#0000FF", "#00FF00", "#FF0000", "#00FF00"))
cluster2_sample <- Phenotype_Wang_Feng_Baseline_clustered %>%
dplyr::filter(., Phenotype == "cluster2") %>%
.$Sample_number
BMIQ_cluster2 <- BMIQ_Baseline %>%
dplyr::select(., cluster2_sample)
## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(cluster2_sample)` instead of `cluster2_sample` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
Non_Responder_sample <- Phenotype_Wang_Feng %>%
dplyr::filter(., Phenotype == "Non_Responder") %>%
.$Sample_number
BMIQ_Non_Responder <- BMIQ_Wang_Feng %>%
dplyr::select(., Non_Responder_sample)
BMIQ_cluster2_Non_Responder <- cbind(BMIQ_cluster2, BMIQ_Non_Responder)
Phenotype_Non_Responder_cluster2 <- data.frame("Sample" = c(cluster2_sample, Non_Responder_sample), "Phenotype" = c(rep("cluster2", length(cluster2_sample)), rep("Non_Responder", length(Non_Responder_sample))))
BMIQ_cluster2_Non_Responder_high_variance <- Focus_high_variance_CpGs(BMIQ_cluster2_Non_Responder)
Focus_global_methylation(match_hit_CpGs_Blueprint_EPIC, anno_EPIC, Phenotype_Non_Responder_cluster2, BMIQ_cluster2_Non_Responder_high_variance, c("cluster2", "Non_Responder"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"))
Focus_Gene_global_methylation("CEBPA", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"))
## Warning: Computation failed in `stat_signif()`:
## valeur manquante là où TRUE / FALSE est requis
Focus_Gene_global_methylation("CPT1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"))
## Warning: Computation failed in `stat_signif()`:
## valeur manquante là où TRUE / FALSE est requis
Focus_Gene_global_methylation("CPT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"))
## Warning: Computation failed in `stat_signif()`:
## valeur manquante là où TRUE / FALSE est requis
Focus_Gene_global_methylation("SLC25A20", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"))
## Warning: Computation failed in `stat_signif()`:
## valeur manquante là où TRUE / FALSE est requis
Focus_Gene_global_methylation("PPARGC1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"))
## Warning: Computation failed in `stat_signif()`:
## valeur manquante là où TRUE / FALSE est requis
Focus_Gene_global_methylation("AKT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"))
## Warning: Computation failed in `stat_signif()`:
## valeur manquante là où TRUE / FALSE est requis
Focus_Gene_global_methylation("PTEN", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"))
## Warning: Computation failed in `stat_signif()`:
## valeur manquante là où TRUE / FALSE est requis
Focus_Gene_global_methylation("CEBPA", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"))
Focus_Gene_global_methylation("CPT1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"))
Focus_Gene_global_methylation("CPT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"))
Focus_Gene_global_methylation("SLC25A20", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"))
Focus_Gene_global_methylation("PPARGC1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"))
Focus_Gene_global_methylation("AKT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"))
Focus_Gene_global_methylation("PTEN", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"))
Focus_gene_promoter_neighborhood_methylation("CEBPA", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
## Warning: Computation failed in `stat_signif()`:
## valeur manquante là où TRUE / FALSE est requis
Focus_gene_promoter_neighborhood_methylation("CPT1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
## Warning: Computation failed in `stat_signif()`:
## valeur manquante là où TRUE / FALSE est requis
Focus_gene_promoter_neighborhood_methylation("CPT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
## Warning: Computation failed in `stat_signif()`:
## valeur manquante là où TRUE / FALSE est requis
Focus_gene_promoter_neighborhood_methylation("SLC25A20", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
## [1] "No Cpgs found in neighbor of promoter of SLC25A20"
## [1] FALSE
Focus_gene_promoter_neighborhood_methylation("PPARGC1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Non-Responder", "Responder"), c("#0000FF", "#555555", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
## [1] "No Cpgs found in neighbor of promoter of PPARGC1A"
## [1] FALSE
Focus_gene_promoter_neighborhood_methylation("AKT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Baseline", "Control"), c("#0000FF", "#555555", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
Focus_gene_promoter_neighborhood_methylation("PTEN", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng, BMIQ_Wang_Feng, c("Baseline", "Control"), c("#0000FF", "#555555", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
Focus_gene_promoter_neighborhood_methylation("CEBPA", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
Focus_gene_promoter_neighborhood_methylation("CPT1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
Focus_gene_promoter_neighborhood_methylation("CPT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
Focus_gene_promoter_neighborhood_methylation("SLC25A20", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
## [1] "No Cpgs found in neighbor of promoter of SLC25A20"
## [1] FALSE
Focus_gene_promoter_neighborhood_methylation("PPARGC1A", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
## [1] "No Cpgs found in neighbor of promoter of PPARGC1A"
## [1] FALSE
Focus_gene_promoter_neighborhood_methylation("AKT2", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
Focus_gene_promoter_neighborhood_methylation("PTEN", match_hit_CpGs_Blueprint_promoter_EPIC, anno_promoter_EPIC, Phenotype_Wang_Feng_Baseline_clustered, BMIQ_Baseline, c("cluster1", "cluster2"), c("#0000FF", "#FF0000", "#FF0000", "#00FF00"), matchit_CpGs_Pchic_EPIC, pchic, match_hit_CpGs_Blueprint_EPIC)
BMIQ_wang_feng_analysis <- Differential_analysis(Phenotype_Wang_Feng$Phenotype, BMIQ_Wang_Feng)
## 1 done
## 2 done
## 3 done
## 4 done
## 5 done
## 6 done
Response_diff_hypermet_in_Non_Responder <- BMIQ_wang_feng_analysis[["Non_Responder-Responder"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC > 0.3)
Response_diff_hypermet_in_Responder <- BMIQ_wang_feng_analysis[["Non_Responder-Responder"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC < 0.3)
Genes_hypermetylated_in_Non_responder <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_Non_Responder$ID, match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Non_responder_enhancer <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_Non_Responder$ID,
matchit_CpGs_Pchic_EPIC,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_EPIC)
Response_diff_hypermet_in_Baseline <- BMIQ_wang_feng_analysis[["Baseline-Control"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC > 0.3)
Response_diff_hypermet_in_Control <- BMIQ_wang_feng_analysis[["Baseline-Control"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC < 0.3)
Genes_hypermetylated_in_Baseline <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_Baseline$ID, match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Baseline_enhancer <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_Control$ID,
matchit_CpGs_Pchic_EPIC,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Non_responder_ego <- enrichGO(
gene = Genes_hypermetylated_in_Non_responder,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Non_responder_enhancer_ego <- enrichGO(
gene = Genes_hypermetylated_in_Non_responder_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Baseline_ego <- enrichGO(
gene = Genes_hypermetylated_in_Baseline,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Baseline_enhancer_ego <- enrichGO(
gene = Genes_hypermetylated_in_Baseline_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Non_responder_promoter_and_enhancer_ego <- enrichGO(
gene = unique(c(Genes_hypermetylated_in_Non_responder, Genes_hypermetylated_in_Non_responder_enhancer)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Baseline_promoter_and_enhancer_ego <- enrichGO(
gene = unique(c(Genes_hypermetylated_in_Baseline, Genes_hypermetylated_in_Baseline_enhancer)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
dotplot(Genes_hypermetylated_in_Non_responder_ego, showCategory = 30, title = "Hypermet NR")
dotplot(Genes_hypermetylated_in_Non_responder_enhancer_ego, showCategory = 30, title = "Hypermet Neighborhood NR")
dotplot(Genes_hypermetylated_in_Non_responder_promoter_and_enhancer_ego, showCategory = 30, title = "Prom & enhancer NR")
dotplot(Genes_hypermetylated_in_Baseline_ego, showCategory = 30, title = "Hypermet Baseline")
dotplot(Genes_hypermetylated_in_Baseline_enhancer_ego, showCategory = 30, title = "Hypermet Neighborhood Baseline")
dotplot(Genes_hypermetylated_in_Baseline_promoter_and_enhancer_ego, showCategory = 30, title = "Prom & enhancer Baseline")
BMIQ_Baseline_analysis <- Differential_analysis(Phenotype_Wang_Feng_Baseline_clustered$Phenotype, BMIQ_Baseline)
## 1 done
Response_diff_hypermet_in_Cluster1 <- BMIQ_Baseline_analysis[["cluster1-cluster2"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC > 0.3)
Response_diff_hypermet_in_Cluster2 <- BMIQ_Baseline_analysis[["cluster1-cluster2"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC < 0.3)
Genes_hypermetylated_in_Cluster1 <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_Cluster1$ID, match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Cluster1_enhancer <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_Cluster1$ID,
matchit_CpGs_Pchic_EPIC,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Cluster2 <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_Cluster2$ID, match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Cluster2_enhancer <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_Cluster2$ID,
matchit_CpGs_Pchic_EPIC,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Cluster1_ego <- enrichGO(
gene = Genes_hypermetylated_in_Cluster1,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Cluster1_enhancer_ego <- enrichGO(
gene = Genes_hypermetylated_in_Cluster1_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Cluster2_ego <- enrichGO(
gene = Genes_hypermetylated_in_Cluster2,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Cluster2_enhancer_ego <- enrichGO(
gene = Genes_hypermetylated_in_Cluster2_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Cluster1_promoter_and_enhancer_ego <- enrichGO(
gene = unique(c(Genes_hypermetylated_in_Cluster1, Genes_hypermetylated_in_Cluster1_enhancer)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Cluster2_promoter_and_enhancer_ego <- enrichGO(
gene = unique(c(Genes_hypermetylated_in_Cluster2, Genes_hypermetylated_in_Cluster2_enhancer)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
#dotplot(Genes_hypermetylated_in_Cluster1_ego, showCategory = 30, title = "Hypermet C1")
dotplot(Genes_hypermetylated_in_Cluster1_enhancer_ego, showCategory = 30, title = "Hypermet Neighborhood C1")
dotplot(Genes_hypermetylated_in_Cluster1_promoter_and_enhancer_ego, showCategory = 30, title = "Prom & enhancer C1")
dotplot(Genes_hypermetylated_in_Cluster2_ego, showCategory = 30, title = "Hypermet C2")
dotplot(Genes_hypermetylated_in_Cluster2_enhancer_ego, showCategory = 30, title = "Hypermet Neighborhood C2", font.size = 9)
dotplot(Genes_hypermetylated_in_Cluster2_promoter_and_enhancer_ego, showCategory = 30, title = "Prom & enhancer C2")
Genes_HM_Baseline_Non_Responder <- unique(intersect(Genes_hypermetylated_in_Non_responder, Genes_hypermetylated_in_Baseline))
Genes_HM_Baseline_Non_Responder_enhancer <- unique(intersect(Genes_hypermetylated_in_Non_responder_enhancer, Genes_hypermetylated_in_Baseline_enhancer))
Genes_HM_Baseline_Non_Responder_Prom_enhancer <- unique(c(Genes_HM_Baseline_Non_Responder, Genes_HM_Baseline_Non_Responder_enhancer))
Genes_HM_Baseline_Non_Responder_ego <- enrichGO(
gene = Genes_HM_Baseline_Non_Responder,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_HM_Baseline_Non_Responder_enhancer_ego <- enrichGO(
gene = Genes_HM_Baseline_Non_Responder_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_HM_Baseline_Non_Responder_Prom_enhancer_ego <- enrichGO(
gene = Genes_HM_Baseline_Non_Responder_Prom_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
dotplot(Genes_HM_Baseline_Non_Responder_ego, showCategory = 30, title = "Hypermet Base&NR")
dotplot(Genes_HM_Baseline_Non_Responder_enhancer_ego, showCategory = 30, title = "Hypermet enhancer Base&NR")
dotplot(Genes_HM_Baseline_Non_Responder_Prom_enhancer_ego, showCategory = 30, title = "Hypermet prom/enhancer Base&NR")
BMIQ_Baseline_cluster1_Responder_analysis <- Differential_analysis(Phenotype_Responder_cluster1$Phenotype, BMIQ_cluster1_Responder)
## 1 done
Response_diff_hypermet_in_Cluster1_vs_Responder <- BMIQ_Baseline_cluster1_Responder_analysis[["cluster1-Responder"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC > 0.3)
Response_diff_hypermet_in_Responder_vs_Cluster1 <- BMIQ_Baseline_cluster1_Responder_analysis[["cluster1-Responder"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC < 0.3)
Genes_hypermetylated_in_Cluster1_vs_Responder <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_Cluster1_vs_Responder$ID, match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Cluster1_vs_Responder_enhancer <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_Cluster1_vs_Responder$ID,
matchit_CpGs_Pchic_EPIC,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Responder_vs_Cluster1 <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_Responder_vs_Cluster1$ID, match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Responder_vs_Cluster1_enhancer <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_Responder_vs_Cluster1$ID,
matchit_CpGs_Pchic_EPIC,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Cluster1_vs_Responder_ego <- enrichGO(
gene = Response_diff_hypermet_in_Cluster1_vs_Responder,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Cluster1_vs_Responder_enhancer_ego <- enrichGO(
gene = Genes_hypermetylated_in_Cluster1_vs_Responder_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Responder_vs_Cluster1_ego <- enrichGO(
gene = Genes_hypermetylated_in_Responder_vs_Cluster1,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Responder_vs_Cluster1_enhancer_ego <- enrichGO(
gene = Genes_hypermetylated_in_Responder_vs_Cluster1_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Cluster1_vs_Responder_promoter_and_enhancer_ego <- enrichGO(
gene = unique(c(Response_diff_hypermet_in_Cluster1_vs_Responder, Genes_hypermetylated_in_Cluster1_vs_Responder_enhancer)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Responder_vs_Cluster1_promoter_and_enhancer_ego <- enrichGO(
gene = unique(c(Genes_hypermetylated_in_Responder_vs_Cluster1, Genes_hypermetylated_in_Responder_vs_Cluster1_enhancer)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
#dotplot(Genes_hypermetylated_in_Cluster1_vs_Responder_ego, showCategory = 30, title = "Hypermet C1")
dotplot(Genes_hypermetylated_in_Cluster1_vs_Responder_enhancer_ego, showCategory = 30, title = "Hypermet Neighborhood C1", font.size = 9)
#dotplot(Genes_hypermetylated_in_Cluster1_vs_Responder_promoter_and_enhancer_ego, showCategory = 30, title = "Prom & enhancer C1", font.size = 9)
dotplot(Genes_hypermetylated_in_Responder_vs_Cluster1_ego, showCategory = 30, title = "Hypermet RES", font.size = 6)
dotplot(Genes_hypermetylated_in_Responder_vs_Cluster1_enhancer_ego, showCategory = 30, title = "Hypermet Neighborhood RES", font.size = 9)
dotplot(Genes_hypermetylated_in_Responder_vs_Cluster1_promoter_and_enhancer_ego, showCategory = 30, title = "Prom & enhancer RES")
BMIQ_Baseline_cluster2_Non_Responder_analysis <- Differential_analysis(Phenotype_Non_Responder_cluster2$Phenotype, BMIQ_cluster2_Non_Responder)
## 1 done
Response_diff_hypermet_in_cluster2_vs_Non_Responder <- BMIQ_Baseline_cluster2_Non_Responder_analysis[["cluster2-Non_Responder"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC > 0.3)
Response_diff_hypermet_in_Non_Responder_vs_cluster2 <- BMIQ_Baseline_cluster2_Non_Responder_analysis[["cluster2-Non_Responder"]] %>%
dplyr::filter(., P.Value < 0.01) %>%
dplyr::filter(., logFC < 0.3)
Genes_hypermetylated_in_cluster2_vs_Non_Responder <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_cluster2_vs_Non_Responder$ID, match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_cluster2_vs_Non_Responder_enhancer <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_cluster2_vs_Non_Responder$ID,
matchit_CpGs_Pchic_EPIC,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Non_Responder_vs_cluster2 <- Look_at_gene_with_CpGs_in_promoter(Response_diff_hypermet_in_Non_Responder_vs_cluster2$ID, match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_Non_Responder_vs_cluster2_enhancer <- Look_at_genes_connected_to_CpGs(Response_diff_hypermet_in_Non_Responder_vs_cluster2$ID,
matchit_CpGs_Pchic_EPIC,
pchic,
matchit_Blueprint_Pchic,
match_hit_CpGs_Blueprint_promoter_EPIC)
Genes_hypermetylated_in_cluster2_vs_Non_Responder_ego <- enrichGO(
gene = Response_diff_hypermet_in_cluster2_vs_Non_Responder,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_cluster2_vs_Non_Responder_enhancer_ego <- enrichGO(
gene = Genes_hypermetylated_in_cluster2_vs_Non_Responder_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Non_Responder_vs_cluster2_ego <- enrichGO(
gene = Genes_hypermetylated_in_Non_Responder_vs_cluster2,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Non_Responder_vs_cluster2_enhancer_ego <- enrichGO(
gene = Genes_hypermetylated_in_Non_Responder_vs_cluster2_enhancer,
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_cluster2_vs_Non_Responder_promoter_and_enhancer_ego <- enrichGO(
gene = unique(c(Response_diff_hypermet_in_cluster2_vs_Non_Responder, Genes_hypermetylated_in_cluster2_vs_Non_Responder_enhancer)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
Genes_hypermetylated_in_Non_Responder_vs_cluster2_promoter_and_enhancer_ego <- enrichGO(
gene = unique(c(Genes_hypermetylated_in_Non_Responder_vs_cluster2, Genes_hypermetylated_in_Non_Responder_vs_cluster2_enhancer)),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_EPIC
)
#dotplot(Genes_hypermetylated_in_cluster2_vs_Non_Responder_ego, showCategory = 30, title = "Hypermet C2")
dotplot(Genes_hypermetylated_in_cluster2_vs_Non_Responder_enhancer_ego, showCategory = 30, title = "Hypermet Neighborhood C2")
#â—‹dotplot(Genes_hypermetylated_in_cluster2_vs_Non_Responder_promoter_and_enhancer_ego, showCategory = 30, title = "Prom & enhancer C2")
dotplot(Genes_hypermetylated_in_Non_Responder_vs_cluster2_ego, showCategory = 30, title = "Hypermet NR")
dotplot(Genes_hypermetylated_in_Non_Responder_vs_cluster2_enhancer_ego, showCategory = 30, title = "Hypermet Neighborhood NR")
dotplot(Genes_hypermetylated_in_Non_Responder_vs_cluster2_promoter_and_enhancer_ego, showCategory = 30, title = "Prom & enhancer NR", font.size = 9)
volcanoplot_methylation(BMIQ_wang_feng_analysis[["Non_Responder-Responder"]], match_hit_CpGs_Blueprint_EPIC, "Non_Responder vs Responder")
volcanoplot_methylation(BMIQ_Baseline_analysis[["cluster1-cluster2"]], match_hit_CpGs_Blueprint_EPIC, "Cluster1 vs Cluster2")
volcanoplot_methylation(BMIQ_Baseline_cluster1_Responder_analysis[["cluster1-Responder"]], match_hit_CpGs_Blueprint_EPIC, "Cluster1 vs Responder")
volcanoplot_methylation(BMIQ_Baseline_cluster2_Non_Responder_analysis[["cluster2-Non_Responder"]], match_hit_CpGs_Blueprint_EPIC, "Cluster2 vs Non Responder")
ann_color_RNA <- list(
Phenotype = c(Baseline = "blue", Relapse = "red"))
Make_heatmap(RNAseq_Wang_Feng, Phenotype_Wang_Feng_RNAseq, "pearson", "Baseline & Relapse", ann_color_RNA)
Make_heatmap(RNAseq_Wang_Feng, Phenotype_Wang_Feng_RNAseq, "spearman", "Baseline & Relapse", ann_color_RNA)
gene_universe_RNAseq_Wang_Feng <- rownames(RNAseq_Wang_Feng) %>%
str_split(., "[|]") %>%
unlist(.) %>%
.[grep("[A-Za-z]", .)]
RNAseq_Wang_Feng_analysis <- Differential_analysis(Phenotype_Wang_Feng_RNAseq$Phenotype, RNAseq_Wang_Feng, "gene expression")
## Warning: Zero sample variances detected, have been offset away from zero
## 1 done
Baseline_Relapse_gene_expression_analysis <- RNAseq_Wang_Feng_analysis[["Baseline-Relapse"]] %>%
dplyr::filter(., )
up_entrez_in_Baseline <- Baseline_Relapse_gene_expression_analysis %>%
dplyr::filter(., logFC > 0 & P.Value < 0.05) %>%
separate_rows(., ID, sep = "[|]") %>%
dplyr::filter(., grepl("[A-Za-z]", ID))
down_entrez_in_Baseline <- Baseline_Relapse_gene_expression_analysis %>%
dplyr::filter(., logFC < 0 & P.Value < 0.05) %>%
separate_rows(., ID, sep = "[|]") %>%
dplyr::filter(., grepl("[A-Za-z]", ID))
Genes_repressed_during_Relapse <- enrichGO(
gene = unique(up_entrez_in_Baseline$ID),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_RNAseq_Wang_Feng
)
Genes_overexpressed_during_Relapse <- enrichGO(
gene = unique(down_entrez_in_Baseline$ID),
keyType = "SYMBOL",
OrgDb = "org.Hs.eg.db",
ont = "ALL",
pAdjustMethod = "none",
universe = gene_universe_RNAseq_Wang_Feng
)
dotplot(Genes_repressed_during_Relapse, showCategory = 30, title = "Genes Down Relapse")
dotplot(Genes_overexpressed_during_Relapse, showCategory = 30, title = "Genes Up Relapse")
volcanoplot_gene_expression(RNAseq_Wang_Feng_analysis[["Baseline-Relapse"]], "Baseline vs Relapse")